Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations74
Missing cells103
Missing cells (%)6.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory72.1 KiB
Average record size in memory998.1 B

Variable types

Numeric7
Text6
Categorical9

Alerts

balls_left is highly overall correlated with first_ings_score and 2 other fieldsHigh correlation
first_ings_score is highly overall correlated with balls_left and 3 other fieldsHigh correlation
first_ings_wkts is highly overall correlated with first_ings_scoreHigh correlation
highscore is highly overall correlated with first_ings_score and 2 other fieldsHigh correlation
match_result is highly overall correlated with balls_left and 4 other fieldsHigh correlation
match_winner is highly overall correlated with match_resultHigh correlation
second_ings_score is highly overall correlated with first_ings_score and 2 other fieldsHigh correlation
second_ings_wkts is highly overall correlated with balls_left and 1 other fieldsHigh correlation
team1 is highly overall correlated with venueHigh correlation
team2 is highly overall correlated with toss_winnerHigh correlation
toss_winner is highly overall correlated with team2High correlation
venue is highly overall correlated with team1High correlation
wb_wickets is highly overall correlated with match_result and 1 other fieldsHigh correlation
stage is highly imbalanced (78.1%) Imbalance
match_result is highly imbalanced (75.5%) Imbalance
toss_winner has 1 (1.4%) missing values Missing
first_ings_score has 1 (1.4%) missing values Missing
first_ings_wkts has 1 (1.4%) missing values Missing
second_ings_score has 2 (2.7%) missing values Missing
second_ings_wkts has 2 (2.7%) missing values Missing
match_winner has 3 (4.1%) missing values Missing
wb_runs has 40 (54.1%) missing values Missing
wb_wickets has 36 (48.6%) missing values Missing
balls_left has 2 (2.7%) missing values Missing
player_of_the_match has 3 (4.1%) missing values Missing
top_scorer has 3 (4.1%) missing values Missing
highscore has 3 (4.1%) missing values Missing
best_bowling has 3 (4.1%) missing values Missing
best_bowling_figure has 3 (4.1%) missing values Missing
match_id is uniformly distributed Uniform
team1 is uniformly distributed Uniform
team2 is uniformly distributed Uniform
match_id has unique values Unique
second_ings_wkts has 2 (2.7%) zeros Zeros
balls_left has 26 (35.1%) zeros Zeros

Reproduction

Analysis started2025-08-13 16:56:07.054775
Analysis finished2025-08-13 16:56:16.675706
Duration9.62 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

match_id
Real number (ℝ)

Uniform  Unique 

Distinct74
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.5
Minimum1
Maximum74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size724.0 B
2025-08-13T16:56:16.858763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.65
Q119.25
median37.5
Q355.75
95-th percentile70.35
Maximum74
Range73
Interquartile range (IQR)36.5

Descriptive statistics

Standard deviation21.505813
Coefficient of variation (CV)0.57348835
Kurtosis-1.2
Mean37.5
Median Absolute Deviation (MAD)18.5
Skewness0
Sum2775
Variance462.5
MonotonicityStrictly increasing
2025-08-13T16:56:17.100040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.4%
2 1
 
1.4%
3 1
 
1.4%
4 1
 
1.4%
5 1
 
1.4%
6 1
 
1.4%
7 1
 
1.4%
8 1
 
1.4%
9 1
 
1.4%
10 1
 
1.4%
Other values (64) 64
86.5%
ValueCountFrequency (%)
1 1
1.4%
2 1
1.4%
3 1
1.4%
4 1
1.4%
5 1
1.4%
6 1
1.4%
7 1
1.4%
8 1
1.4%
9 1
1.4%
10 1
1.4%
ValueCountFrequency (%)
74 1
1.4%
73 1
1.4%
72 1
1.4%
71 1
1.4%
70 1
1.4%
69 1
1.4%
68 1
1.4%
67 1
1.4%
66 1
1.4%
65 1
1.4%

date
Text

Distinct62
Distinct (%)83.8%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
2025-08-13T16:56:17.396277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length13
Median length13
Mean length12.216216
Min length10

Characters and Unicode

Total characters904
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)67.6%

Sample

1st rowMarch 22,2025
2nd rowMarch 23,2025
3rd rowMarch 23,2025
4th rowMarch 24,2025
5th rowMarch 25,2025
ValueCountFrequency (%)
april 37
25.0%
may 23
15.5%
march 12
 
8.1%
23,2025 4
 
2.7%
27,2025 4
 
2.7%
30,2025 4
 
2.7%
25,2025 4
 
2.7%
18,2025 3
 
2.0%
19,2025 3
 
2.0%
20,2025 3
 
2.0%
Other values (32) 51
34.5%
2025-08-13T16:56:17.737978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 186
20.6%
0 93
10.3%
5 82
9.1%
74
 
8.2%
, 74
 
8.2%
r 49
 
5.4%
i 37
 
4.1%
p 37
 
4.1%
A 37
 
4.1%
l 37
 
4.1%
Other values (16) 198
21.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 904
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 186
20.6%
0 93
10.3%
5 82
9.1%
74
 
8.2%
, 74
 
8.2%
r 49
 
5.4%
i 37
 
4.1%
p 37
 
4.1%
A 37
 
4.1%
l 37
 
4.1%
Other values (16) 198
21.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 904
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 186
20.6%
0 93
10.3%
5 82
9.1%
74
 
8.2%
, 74
 
8.2%
r 49
 
5.4%
i 37
 
4.1%
p 37
 
4.1%
A 37
 
4.1%
l 37
 
4.1%
Other values (16) 198
21.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 904
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 186
20.6%
0 93
10.3%
5 82
9.1%
74
 
8.2%
, 74
 
8.2%
r 49
 
5.4%
i 37
 
4.1%
p 37
 
4.1%
A 37
 
4.1%
l 37
 
4.1%
Other values (16) 198
21.9%

venue
Categorical

High correlation 

Distinct13
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
Narendra Modi Stadium, Ahmedabad
Ekana Cricket Stadium, Lucknow
Eden Gardens, Kolkata
Wankhede Stadium, Mumbai
Arun Jaitley Stadium, Delhi
Other values (8)
36 

Length

Max length45
Median length33
Mean length30.527027
Min length21

Characters and Unicode

Total characters2259
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.4%

Sample

1st rowEden Gardens, Kolkata
2nd rowRajiv Gandhi International Stadium, Hyderabad
3rd rowMA Chidambaram Stadium, Chennai
4th rowACA-VDCA Cricket Stadium, Vishakhapatnam
5th rowNarendra Modi Stadium, Ahmedabad

Common Values

ValueCountFrequency (%)
Narendra Modi Stadium, Ahmedabad 9
12.2%
Ekana Cricket Stadium, Lucknow 8
10.8%
Eden Gardens, Kolkata 7
9.5%
Wankhede Stadium, Mumbai 7
9.5%
Arun Jaitley Stadium, Delhi 7
9.5%
Sawai Mansingh Stadium, Jaipur 7
9.5%
New PCA Cricket Stadium, Mullanpur 6
8.1%
Rajiv Gandhi International Stadium, Hyderabad 6
8.1%
MA Chidambaram Stadium, Chennai 6
8.1%
M. Chinnaswamy Stadium, Bangalore 6
8.1%
Other values (3) 5
6.8%

Length

2025-08-13T16:56:17.870762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
stadium 67
23.0%
cricket 16
 
5.5%
narendra 9
 
3.1%
ahmedabad 9
 
3.1%
modi 9
 
3.1%
ekana 8
 
2.7%
lucknow 8
 
2.7%
eden 7
 
2.4%
gardens 7
 
2.4%
kolkata 7
 
2.4%
Other values (27) 144
49.5%

Most occurring characters

ValueCountFrequency (%)
a 303
 
13.4%
217
 
9.6%
i 174
 
7.7%
d 148
 
6.6%
n 129
 
5.7%
t 113
 
5.0%
u 110
 
4.9%
e 106
 
4.7%
m 104
 
4.6%
r 90
 
4.0%
Other values (34) 765
33.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2259
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 303
 
13.4%
217
 
9.6%
i 174
 
7.7%
d 148
 
6.6%
n 129
 
5.7%
t 113
 
5.0%
u 110
 
4.9%
e 106
 
4.7%
m 104
 
4.6%
r 90
 
4.0%
Other values (34) 765
33.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2259
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 303
 
13.4%
217
 
9.6%
i 174
 
7.7%
d 148
 
6.6%
n 129
 
5.7%
t 113
 
5.0%
u 110
 
4.9%
e 106
 
4.7%
m 104
 
4.6%
r 90
 
4.0%
Other values (34) 765
33.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2259
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 303
 
13.4%
217
 
9.6%
i 174
 
7.7%
d 148
 
6.6%
n 129
 
5.7%
t 113
 
5.0%
u 110
 
4.9%
e 106
 
4.7%
m 104
 
4.6%
r 90
 
4.0%
Other values (34) 765
33.9%

team1
Categorical

High correlation  Uniform 

Distinct10
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
PBKS
KKR
RCB
GT
RR
Other values (5)
34 

Length

Max length4
Median length3
Mean length2.7297297
Min length2

Characters and Unicode

Total characters202
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKKR
2nd rowSRH
3rd rowCSK
4th rowDC
5th rowGT

Common Values

ValueCountFrequency (%)
PBKS 9
12.2%
KKR 8
10.8%
RCB 8
10.8%
GT 8
10.8%
RR 7
9.5%
CSK 7
9.5%
LSG 7
9.5%
DC 7
9.5%
MI 7
9.5%
SRH 6
8.1%

Length

2025-08-13T16:56:17.996972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-13T16:56:18.149489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
pbks 9
12.2%
kkr 8
10.8%
rcb 8
10.8%
gt 8
10.8%
rr 7
9.5%
csk 7
9.5%
lsg 7
9.5%
dc 7
9.5%
mi 7
9.5%
srh 6
8.1%

Most occurring characters

ValueCountFrequency (%)
R 36
17.8%
K 32
15.8%
S 29
14.4%
C 22
10.9%
B 17
8.4%
G 15
7.4%
P 9
 
4.5%
T 8
 
4.0%
L 7
 
3.5%
D 7
 
3.5%
Other values (3) 20
9.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 202
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 36
17.8%
K 32
15.8%
S 29
14.4%
C 22
10.9%
B 17
8.4%
G 15
7.4%
P 9
 
4.5%
T 8
 
4.0%
L 7
 
3.5%
D 7
 
3.5%
Other values (3) 20
9.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 202
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 36
17.8%
K 32
15.8%
S 29
14.4%
C 22
10.9%
B 17
8.4%
G 15
7.4%
P 9
 
4.5%
T 8
 
4.0%
L 7
 
3.5%
D 7
 
3.5%
Other values (3) 20
9.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 202
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 36
17.8%
K 32
15.8%
S 29
14.4%
C 22
10.9%
B 17
8.4%
G 15
7.4%
P 9
 
4.5%
T 8
 
4.0%
L 7
 
3.5%
D 7
 
3.5%
Other values (3) 20
9.9%

team2
Categorical

High correlation  Uniform 

Distinct10
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
MI
RCB
SRH
PBKS
RR
Other values (5)
34 

Length

Max length4
Median length3
Mean length2.7027027
Min length2

Characters and Unicode

Total characters200
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRCB
2nd rowRR
3rd rowMI
4th rowLSG
5th rowPBKS

Common Values

ValueCountFrequency (%)
MI 9
12.2%
RCB 8
10.8%
SRH 8
10.8%
PBKS 8
10.8%
RR 7
9.5%
LSG 7
9.5%
GT 7
9.5%
CSK 7
9.5%
DC 7
9.5%
KKR 6
8.1%

Length

2025-08-13T16:56:18.324707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-13T16:56:18.450391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
mi 9
12.2%
rcb 8
10.8%
srh 8
10.8%
pbks 8
10.8%
rr 7
9.5%
lsg 7
9.5%
gt 7
9.5%
csk 7
9.5%
dc 7
9.5%
kkr 6
8.1%

Most occurring characters

ValueCountFrequency (%)
R 36
18.0%
S 30
15.0%
K 27
13.5%
C 22
11.0%
B 16
8.0%
G 14
 
7.0%
M 9
 
4.5%
I 9
 
4.5%
H 8
 
4.0%
P 8
 
4.0%
Other values (3) 21
10.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 36
18.0%
S 30
15.0%
K 27
13.5%
C 22
11.0%
B 16
8.0%
G 14
 
7.0%
M 9
 
4.5%
I 9
 
4.5%
H 8
 
4.0%
P 8
 
4.0%
Other values (3) 21
10.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 36
18.0%
S 30
15.0%
K 27
13.5%
C 22
11.0%
B 16
8.0%
G 14
 
7.0%
M 9
 
4.5%
I 9
 
4.5%
H 8
 
4.0%
P 8
 
4.0%
Other values (3) 21
10.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 36
18.0%
S 30
15.0%
K 27
13.5%
C 22
11.0%
B 16
8.0%
G 14
 
7.0%
M 9
 
4.5%
I 9
 
4.5%
H 8
 
4.0%
P 8
 
4.0%
Other values (3) 21
10.5%

stage
Categorical

Imbalance 

Distinct3
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
League
70 
Playoffs
 
3
Final
 
1

Length

Max length8
Median length6
Mean length6.0675676
Min length5

Characters and Unicode

Total characters449
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.4%

Sample

1st rowLeague
2nd rowLeague
3rd rowLeague
4th rowLeague
5th rowLeague

Common Values

ValueCountFrequency (%)
League 70
94.6%
Playoffs 3
 
4.1%
Final 1
 
1.4%

Length

2025-08-13T16:56:18.620966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-13T16:56:18.721546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
league 70
94.6%
playoffs 3
 
4.1%
final 1
 
1.4%

Most occurring characters

ValueCountFrequency (%)
e 140
31.2%
a 74
16.5%
L 70
15.6%
g 70
15.6%
u 70
15.6%
f 6
 
1.3%
l 4
 
0.9%
P 3
 
0.7%
y 3
 
0.7%
o 3
 
0.7%
Other values (4) 6
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 449
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 140
31.2%
a 74
16.5%
L 70
15.6%
g 70
15.6%
u 70
15.6%
f 6
 
1.3%
l 4
 
0.9%
P 3
 
0.7%
y 3
 
0.7%
o 3
 
0.7%
Other values (4) 6
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 449
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 140
31.2%
a 74
16.5%
L 70
15.6%
g 70
15.6%
u 70
15.6%
f 6
 
1.3%
l 4
 
0.9%
P 3
 
0.7%
y 3
 
0.7%
o 3
 
0.7%
Other values (4) 6
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 449
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 140
31.2%
a 74
16.5%
L 70
15.6%
g 70
15.6%
u 70
15.6%
f 6
 
1.3%
l 4
 
0.9%
P 3
 
0.7%
y 3
 
0.7%
o 3
 
0.7%
Other values (4) 6
 
1.3%

toss_winner
Categorical

High correlation  Missing 

Distinct10
Distinct (%)13.7%
Missing1
Missing (%)1.4%
Memory size4.5 KiB
PBKS
12 
DC
SRH
MI
RR
Other values (5)
31 

Length

Max length4
Median length3
Mean length2.7671233
Min length2

Characters and Unicode

Total characters202
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRCB
2nd rowRR
3rd rowCSK
4th rowDC
5th rowGT

Common Values

ValueCountFrequency (%)
PBKS 12
16.2%
DC 8
10.8%
SRH 8
10.8%
MI 7
9.5%
RR 7
9.5%
RCB 7
9.5%
GT 7
9.5%
CSK 6
8.1%
KKR 6
8.1%
LSG 5
6.8%
(Missing) 1
 
1.4%

Length

2025-08-13T16:56:18.836268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-13T16:56:18.958626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
pbks 12
16.4%
dc 8
11.0%
srh 8
11.0%
mi 7
9.6%
rr 7
9.6%
rcb 7
9.6%
gt 7
9.6%
csk 6
8.2%
kkr 6
8.2%
lsg 5
6.8%

Most occurring characters

ValueCountFrequency (%)
R 35
17.3%
S 31
15.3%
K 30
14.9%
C 21
10.4%
B 19
9.4%
G 12
 
5.9%
P 12
 
5.9%
D 8
 
4.0%
H 8
 
4.0%
M 7
 
3.5%
Other values (3) 19
9.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 202
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 35
17.3%
S 31
15.3%
K 30
14.9%
C 21
10.4%
B 19
9.4%
G 12
 
5.9%
P 12
 
5.9%
D 8
 
4.0%
H 8
 
4.0%
M 7
 
3.5%
Other values (3) 19
9.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 202
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 35
17.3%
S 31
15.3%
K 30
14.9%
C 21
10.4%
B 19
9.4%
G 12
 
5.9%
P 12
 
5.9%
D 8
 
4.0%
H 8
 
4.0%
M 7
 
3.5%
Other values (3) 19
9.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 202
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 35
17.3%
S 31
15.3%
K 30
14.9%
C 21
10.4%
B 19
9.4%
G 12
 
5.9%
P 12
 
5.9%
D 8
 
4.0%
H 8
 
4.0%
M 7
 
3.5%
Other values (3) 19
9.4%

toss_decision
Categorical

Distinct2
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
Bowl
61 
Bat
13 

Length

Max length4
Median length4
Mean length3.8243243
Min length3

Characters and Unicode

Total characters283
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBowl
2nd rowBowl
3rd rowBowl
4th rowBowl
5th rowBowl

Common Values

ValueCountFrequency (%)
Bowl 61
82.4%
Bat 13
 
17.6%

Length

2025-08-13T16:56:19.113694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-13T16:56:19.187112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
bowl 61
82.4%
bat 13
 
17.6%

Most occurring characters

ValueCountFrequency (%)
B 74
26.1%
o 61
21.6%
w 61
21.6%
l 61
21.6%
a 13
 
4.6%
t 13
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 283
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 74
26.1%
o 61
21.6%
w 61
21.6%
l 61
21.6%
a 13
 
4.6%
t 13
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 283
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 74
26.1%
o 61
21.6%
w 61
21.6%
l 61
21.6%
a 13
 
4.6%
t 13
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 283
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 74
26.1%
o 61
21.6%
w 61
21.6%
l 61
21.6%
a 13
 
4.6%
t 13
 
4.6%

first_ings_score
Real number (ℝ)

High correlation  Missing 

Distinct56
Distinct (%)76.7%
Missing1
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean189.75342
Minimum95
Maximum286
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size724.0 B
2025-08-13T16:56:19.581729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum95
5-th percentile114
Q1166
median196
Q3213
95-th percentile240
Maximum286
Range191
Interquartile range (IQR)47

Descriptive statistics

Standard deviation37.489547
Coefficient of variation (CV)0.1975698
Kurtosis0.61883518
Mean189.75342
Median Absolute Deviation (MAD)23
Skewness-0.30442583
Sum13852
Variance1405.4661
MonotonicityNot monotonic
2025-08-13T16:56:19.749995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
205 4
 
5.4%
203 3
 
4.1%
180 3
 
4.1%
190 3
 
4.1%
163 2
 
2.7%
219 2
 
2.7%
155 2
 
2.7%
196 2
 
2.7%
209 2
 
2.7%
217 2
 
2.7%
Other values (46) 48
64.9%
ValueCountFrequency (%)
95 1
1.4%
101 1
1.4%
103 1
1.4%
111 1
1.4%
116 1
1.4%
133 1
1.4%
143 1
1.4%
151 1
1.4%
152 1
1.4%
154 1
1.4%
ValueCountFrequency (%)
286 1
1.4%
278 1
1.4%
245 1
1.4%
243 1
1.4%
238 1
1.4%
236 1
1.4%
235 1
1.4%
231 1
1.4%
230 1
1.4%
228 1
1.4%

first_ings_wkts
Real number (ℝ)

High correlation  Missing 

Distinct9
Distinct (%)12.3%
Missing1
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean6.4520548
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size724.0 B
2025-08-13T16:56:19.872861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q15
median6
Q38
95-th percentile10
Maximum10
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.1084558
Coefficient of variation (CV)0.32678827
Kurtosis-0.76366812
Mean6.4520548
Median Absolute Deviation (MAD)2
Skewness-0.082005805
Sum471
Variance4.445586
MonotonicityNot monotonic
2025-08-13T16:56:19.979062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5 14
18.9%
6 13
17.6%
8 12
16.2%
9 8
10.8%
7 8
10.8%
10 6
8.1%
4 5
 
6.8%
3 5
 
6.8%
2 2
 
2.7%
(Missing) 1
 
1.4%
ValueCountFrequency (%)
2 2
 
2.7%
3 5
 
6.8%
4 5
 
6.8%
5 14
18.9%
6 13
17.6%
7 8
10.8%
8 12
16.2%
9 8
10.8%
10 6
8.1%
ValueCountFrequency (%)
10 6
8.1%
9 8
10.8%
8 12
16.2%
7 8
10.8%
6 13
17.6%
5 14
18.9%
4 5
 
6.8%
3 5
 
6.8%
2 2
 
2.7%

second_ings_score
Real number (ℝ)

High correlation  Missing 

Distinct51
Distinct (%)70.8%
Missing2
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean174.01389
Minimum7
Maximum247
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size724.0 B
2025-08-13T16:56:20.117595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile106.55
Q1158
median177
Q3201.25
95-th percentile230.9
Maximum247
Range240
Interquartile range (IQR)43.25

Descriptive statistics

Standard deviation38.805217
Coefficient of variation (CV)0.22300069
Kurtosis3.8502523
Mean174.01389
Median Absolute Deviation (MAD)22
Skewness-1.2679678
Sum12529
Variance1505.8449
MonotonicityNot monotonic
2025-08-13T16:56:20.270838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
177 3
 
4.1%
159 3
 
4.1%
211 2
 
2.7%
158 2
 
2.7%
121 2
 
2.7%
153 2
 
2.7%
146 2
 
2.7%
193 2
 
2.7%
155 2
 
2.7%
209 2
 
2.7%
Other values (41) 50
67.6%
ValueCountFrequency (%)
7 1
1.4%
95 1
1.4%
98 1
1.4%
106 1
1.4%
107 1
1.4%
117 1
1.4%
120 1
1.4%
121 2
2.7%
146 2
2.7%
147 2
2.7%
ValueCountFrequency (%)
247 1
1.4%
242 1
1.4%
234 1
1.4%
232 1
1.4%
230 1
1.4%
212 1
1.4%
211 2
2.7%
209 2
2.7%
208 2
2.7%
207 1
1.4%

second_ings_wkts
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct11
Distinct (%)15.3%
Missing2
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean5.5138889
Minimum0
Maximum10
Zeros2
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size724.0 B
2025-08-13T16:56:20.402769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.55
Q13
median5
Q38
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8826487
Coefficient of variation (CV)0.52279775
Kurtosis-1.0251697
Mean5.5138889
Median Absolute Deviation (MAD)2
Skewness0.11228863
Sum397
Variance8.3096635
MonotonicityNot monotonic
2025-08-13T16:56:20.503623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
5 11
14.9%
10 10
13.5%
2 9
12.2%
6 8
10.8%
4 8
10.8%
3 7
9.5%
9 6
8.1%
7 5
6.8%
8 4
 
5.4%
1 2
 
2.7%
ValueCountFrequency (%)
0 2
 
2.7%
1 2
 
2.7%
2 9
12.2%
3 7
9.5%
4 8
10.8%
5 11
14.9%
6 8
10.8%
7 5
6.8%
8 4
 
5.4%
9 6
8.1%
ValueCountFrequency (%)
10 10
13.5%
9 6
8.1%
8 4
 
5.4%
7 5
6.8%
6 8
10.8%
5 11
14.9%
4 8
10.8%
3 7
9.5%
2 9
12.2%
1 2
 
2.7%

match_result
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
completed
71 
tied
 
3

Length

Max length9
Median length9
Mean length8.7972973
Min length4

Characters and Unicode

Total characters651
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcompleted
2nd rowcompleted
3rd rowcompleted
4th rowcompleted
5th rowcompleted

Common Values

ValueCountFrequency (%)
completed 71
95.9%
tied 3
 
4.1%

Length

2025-08-13T16:56:20.617518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-13T16:56:20.690073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
completed 71
95.9%
tied 3
 
4.1%

Most occurring characters

ValueCountFrequency (%)
e 145
22.3%
d 74
11.4%
t 74
11.4%
c 71
10.9%
o 71
10.9%
l 71
10.9%
p 71
10.9%
m 71
10.9%
i 3
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 651
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 145
22.3%
d 74
11.4%
t 74
11.4%
c 71
10.9%
o 71
10.9%
l 71
10.9%
p 71
10.9%
m 71
10.9%
i 3
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 651
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 145
22.3%
d 74
11.4%
t 74
11.4%
c 71
10.9%
o 71
10.9%
l 71
10.9%
p 71
10.9%
m 71
10.9%
i 3
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 651
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 145
22.3%
d 74
11.4%
t 74
11.4%
c 71
10.9%
o 71
10.9%
l 71
10.9%
p 71
10.9%
m 71
10.9%
i 3
 
0.5%

match_winner
Categorical

High correlation  Missing 

Distinct10
Distinct (%)14.1%
Missing3
Missing (%)4.1%
Memory size4.5 KiB
PBKS
11 
RCB
10 
GT
MI
DC
Other values (5)
25 

Length

Max length4
Median length3
Mean length2.7464789
Min length2

Characters and Unicode

Total characters195
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRCB
2nd rowSRH
3rd rowCSK
4th rowDC
5th rowPBKS

Common Values

ValueCountFrequency (%)
PBKS 11
14.9%
RCB 10
13.5%
GT 9
12.2%
MI 8
10.8%
DC 8
10.8%
LSG 6
8.1%
SRH 6
8.1%
KKR 5
6.8%
CSK 4
 
5.4%
RR 4
 
5.4%
(Missing) 3
 
4.1%

Length

2025-08-13T16:56:20.813198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-13T16:56:20.941987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
pbks 11
15.5%
rcb 10
14.1%
gt 9
12.7%
mi 8
11.3%
dc 8
11.3%
lsg 6
8.5%
srh 6
8.5%
kkr 5
7.0%
csk 4
 
5.6%
rr 4
 
5.6%

Most occurring characters

ValueCountFrequency (%)
R 29
14.9%
S 27
13.8%
K 25
12.8%
C 22
11.3%
B 21
10.8%
G 15
7.7%
P 11
 
5.6%
T 9
 
4.6%
M 8
 
4.1%
I 8
 
4.1%
Other values (3) 20
10.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 195
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 29
14.9%
S 27
13.8%
K 25
12.8%
C 22
11.3%
B 21
10.8%
G 15
7.7%
P 11
 
5.6%
T 9
 
4.6%
M 8
 
4.1%
I 8
 
4.1%
Other values (3) 20
10.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 195
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 29
14.9%
S 27
13.8%
K 25
12.8%
C 22
11.3%
B 21
10.8%
G 15
7.7%
P 11
 
5.6%
T 9
 
4.6%
M 8
 
4.1%
I 8
 
4.1%
Other values (3) 20
10.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 195
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 29
14.9%
S 27
13.8%
K 25
12.8%
C 22
11.3%
B 21
10.8%
G 15
7.7%
P 11
 
5.6%
T 9
 
4.6%
M 8
 
4.1%
I 8
 
4.1%
Other values (3) 20
10.3%

wb_runs
Text

Missing 

Distinct28
Distinct (%)82.4%
Missing40
Missing (%)54.1%
Memory size3.3 KiB
2025-08-13T16:56:21.160199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length2
Mean length2.1176471
Min length1

Characters and Unicode

Total characters72
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)67.6%

Sample

1st row44
2nd row11
3rd row50
4th row36
5th row6
ValueCountFrequency (%)
12 3
 
8.6%
11 2
 
5.7%
6 2
 
5.7%
50 2
 
5.7%
2 2
 
5.7%
36 1
 
2.9%
80 1
 
2.9%
44 1
 
2.9%
4 1
 
2.9%
18 1
 
2.9%
Other values (19) 19
54.3%
2025-08-13T16:56:21.457502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 15
20.8%
2 8
11.1%
0 8
11.1%
3 7
9.7%
5 6
 
8.3%
4 6
 
8.3%
8 5
 
6.9%
6 4
 
5.6%
r 2
 
2.8%
e 2
 
2.8%
Other values (8) 9
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 72
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 15
20.8%
2 8
11.1%
0 8
11.1%
3 7
9.7%
5 6
 
8.3%
4 6
 
8.3%
8 5
 
6.9%
6 4
 
5.6%
r 2
 
2.8%
e 2
 
2.8%
Other values (8) 9
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 72
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 15
20.8%
2 8
11.1%
0 8
11.1%
3 7
9.7%
5 6
 
8.3%
4 6
 
8.3%
8 5
 
6.9%
6 4
 
5.6%
r 2
 
2.8%
e 2
 
2.8%
Other values (8) 9
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 72
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 15
20.8%
2 8
11.1%
0 8
11.1%
3 7
9.7%
5 6
 
8.3%
4 6
 
8.3%
8 5
 
6.9%
6 4
 
5.6%
r 2
 
2.8%
e 2
 
2.8%
Other values (8) 9
12.5%

wb_wickets
Categorical

High correlation  Missing 

Distinct11
Distinct (%)28.9%
Missing36
Missing (%)48.6%
Memory size4.5 KiB
8
6
7
5
4
Other values (6)

Length

Max length10
Median length1
Mean length1.2631579
Min length1

Characters and Unicode

Total characters48
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)13.2%

Sample

1st row7
2nd row4
3rd row1
4th row8
5th row5

Common Values

ValueCountFrequency (%)
8 9
 
12.2%
6 7
 
9.5%
7 7
 
9.5%
5 5
 
6.8%
4 3
 
4.1%
9 2
 
2.7%
1 1
 
1.4%
super over 1
 
1.4%
3 1
 
1.4%
2 1
 
1.4%
(Missing) 36
48.6%

Length

2025-08-13T16:56:21.590233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
8 9
23.1%
6 7
17.9%
7 7
17.9%
5 5
12.8%
4 3
 
7.7%
9 2
 
5.1%
1 1
 
2.6%
super 1
 
2.6%
over 1
 
2.6%
3 1
 
2.6%
Other values (2) 2
 
5.1%

Most occurring characters

ValueCountFrequency (%)
8 9
18.8%
6 7
14.6%
7 7
14.6%
5 5
10.4%
4 3
 
6.2%
9 2
 
4.2%
1 2
 
4.2%
e 2
 
4.2%
r 2
 
4.2%
s 1
 
2.1%
Other values (8) 8
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 48
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 9
18.8%
6 7
14.6%
7 7
14.6%
5 5
10.4%
4 3
 
6.2%
9 2
 
4.2%
1 2
 
4.2%
e 2
 
4.2%
r 2
 
4.2%
s 1
 
2.1%
Other values (8) 8
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 48
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 9
18.8%
6 7
14.6%
7 7
14.6%
5 5
10.4%
4 3
 
6.2%
9 2
 
4.2%
1 2
 
4.2%
e 2
 
4.2%
r 2
 
4.2%
s 1
 
2.1%
Other values (8) 8
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 48
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 9
18.8%
6 7
14.6%
7 7
14.6%
5 5
10.4%
4 3
 
6.2%
9 2
 
4.2%
1 2
 
4.2%
e 2
 
4.2%
r 2
 
4.2%
s 1
 
2.1%
Other values (8) 8
16.7%

balls_left
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct26
Distinct (%)36.1%
Missing2
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean10.819444
Minimum0
Maximum114
Zeros26
Zeros (%)35.1%
Negative0
Negative (%)0.0%
Memory size724.0 B
2025-08-13T16:56:21.701822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5.5
Q313.5
95-th percentile35.3
Maximum114
Range114
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation17.674942
Coefficient of variation (CV)1.6336275
Kurtosis16.724088
Mean10.819444
Median Absolute Deviation (MAD)5.5
Skewness3.5436021
Sum779
Variance312.40356
MonotonicityNot monotonic
2025-08-13T16:56:21.847280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 26
35.1%
3 4
 
5.4%
9 4
 
5.4%
6 3
 
4.1%
8 3
 
4.1%
13 3
 
4.1%
4 2
 
2.7%
20 2
 
2.7%
22 2
 
2.7%
23 2
 
2.7%
Other values (16) 21
28.4%
ValueCountFrequency (%)
0 26
35.1%
1 1
 
1.4%
2 2
 
2.7%
3 4
 
5.4%
4 2
 
2.7%
5 1
 
1.4%
6 3
 
4.1%
7 1
 
1.4%
8 3
 
4.1%
9 4
 
5.4%
ValueCountFrequency (%)
114 1
1.4%
60 1
1.4%
59 1
1.4%
43 1
1.4%
29 1
1.4%
26 2
2.7%
25 1
1.4%
24 1
1.4%
23 2
2.7%
22 2
2.7%

player_of_the_match
Text

Missing 

Distinct53
Distinct (%)74.6%
Missing3
Missing (%)4.1%
Memory size5.1 KiB
2025-08-13T16:56:22.159654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length16
Mean length12.774648
Min length8

Characters and Unicode

Total characters907
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)52.1%

Sample

1st rowKrunal Pandya
2nd rowIshan Kishan
3rd rowNoor Ahmad
4th rowAshutosh Sharma
5th rowShreyas Iyer
ValueCountFrequency (%)
sharma 8
 
5.6%
krunal 3
 
2.1%
pandya 3
 
2.1%
iyer 3
 
2.1%
shreyas 3
 
2.1%
mitchell 3
 
2.1%
singh 3
 
2.1%
kishan 2
 
1.4%
prasidh 2
 
1.4%
noor 2
 
1.4%
Other values (87) 111
77.6%
2025-08-13T16:56:22.612294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 119
 
13.1%
h 75
 
8.3%
72
 
7.9%
r 71
 
7.8%
i 58
 
6.4%
n 44
 
4.9%
s 43
 
4.7%
e 40
 
4.4%
l 39
 
4.3%
S 34
 
3.7%
Other values (35) 312
34.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 907
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 119
 
13.1%
h 75
 
8.3%
72
 
7.9%
r 71
 
7.8%
i 58
 
6.4%
n 44
 
4.9%
s 43
 
4.7%
e 40
 
4.4%
l 39
 
4.3%
S 34
 
3.7%
Other values (35) 312
34.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 907
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 119
 
13.1%
h 75
 
8.3%
72
 
7.9%
r 71
 
7.8%
i 58
 
6.4%
n 44
 
4.9%
s 43
 
4.7%
e 40
 
4.4%
l 39
 
4.3%
S 34
 
3.7%
Other values (35) 312
34.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 907
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 119
 
13.1%
h 75
 
8.3%
72
 
7.9%
r 71
 
7.8%
i 58
 
6.4%
n 44
 
4.9%
s 43
 
4.7%
e 40
 
4.4%
l 39
 
4.3%
S 34
 
3.7%
Other values (35) 312
34.4%

top_scorer
Text

Missing 

Distinct41
Distinct (%)57.7%
Missing3
Missing (%)4.1%
Memory size5.1 KiB
2025-08-13T16:56:22.929853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length20
Median length16
Mean length13.056338
Min length8

Characters and Unicode

Total characters927
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)29.6%

Sample

1st rowVirat Kohli
2nd rowIshan Kishan
3rd rowRachin Ravindra
4th rowNicholas Pooran
5th rowShreyas Iyer
ValueCountFrequency (%)
virat 4
 
2.8%
kohli 4
 
2.8%
nicholas 4
 
2.8%
pooran 4
 
2.8%
sharma 4
 
2.8%
kishan 3
 
2.1%
sai 3
 
2.1%
sudarshan 3
 
2.1%
ishan 3
 
2.1%
gill 3
 
2.1%
Other values (71) 109
75.7%
2025-08-13T16:56:23.374422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 127
13.7%
i 77
 
8.3%
73
 
7.9%
h 73
 
7.9%
s 56
 
6.0%
n 55
 
5.9%
r 54
 
5.8%
l 42
 
4.5%
e 34
 
3.7%
S 27
 
2.9%
Other values (35) 309
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 927
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 127
13.7%
i 77
 
8.3%
73
 
7.9%
h 73
 
7.9%
s 56
 
6.0%
n 55
 
5.9%
r 54
 
5.8%
l 42
 
4.5%
e 34
 
3.7%
S 27
 
2.9%
Other values (35) 309
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 927
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 127
13.7%
i 77
 
8.3%
73
 
7.9%
h 73
 
7.9%
s 56
 
6.0%
n 55
 
5.9%
r 54
 
5.8%
l 42
 
4.5%
e 34
 
3.7%
S 27
 
2.9%
Other values (35) 309
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 927
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 127
13.7%
i 77
 
8.3%
73
 
7.9%
h 73
 
7.9%
s 56
 
6.0%
n 55
 
5.9%
r 54
 
5.8%
l 42
 
4.5%
e 34
 
3.7%
S 27
 
2.9%
Other values (35) 309
33.3%

highscore
Real number (ℝ)

High correlation  Missing 

Distinct44
Distinct (%)62.0%
Missing3
Missing (%)4.1%
Infinite0
Infinite (%)0.0%
Mean74.549296
Minimum37
Maximum141
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size724.0 B
2025-08-13T16:56:23.515458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile47
Q161
median73
Q388.5
95-th percentile107
Maximum141
Range104
Interquartile range (IQR)27.5

Descriptive statistics

Standard deviation20.088226
Coefficient of variation (CV)0.26946232
Kurtosis0.65751389
Mean74.549296
Median Absolute Deviation (MAD)14
Skewness0.7245618
Sum5293
Variance403.53682
MonotonicityNot monotonic
2025-08-13T16:56:23.672519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
73 5
 
6.8%
61 4
 
5.4%
70 3
 
4.1%
67 3
 
4.1%
97 3
 
4.1%
57 3
 
4.1%
63 2
 
2.7%
51 2
 
2.7%
74 2
 
2.7%
81 2
 
2.7%
Other values (34) 42
56.8%
(Missing) 3
 
4.1%
ValueCountFrequency (%)
37 1
 
1.4%
40 1
 
1.4%
44 2
2.7%
50 1
 
1.4%
51 2
2.7%
52 1
 
1.4%
53 1
 
1.4%
56 1
 
1.4%
57 3
4.1%
58 2
2.7%
ValueCountFrequency (%)
141 1
 
1.4%
118 1
 
1.4%
117 1
 
1.4%
108 1
 
1.4%
106 1
 
1.4%
105 1
 
1.4%
103 1
 
1.4%
101 1
 
1.4%
97 3
4.1%
95 1
 
1.4%

best_bowling
Text

Missing 

Distinct42
Distinct (%)59.2%
Missing3
Missing (%)4.1%
Memory size5.1 KiB
2025-08-13T16:56:23.995425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length18
Median length16
Mean length13.56338
Min length10

Characters and Unicode

Total characters963
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)33.8%

Sample

1st rowKrunal Pandya
2nd rowTushar Deshpande
3rd rowNoor Ahmad
4th rowMitchell Starc
5th rowSai Kishore
ValueCountFrequency (%)
prasidh 5
 
3.5%
krishna 5
 
3.5%
pandya 5
 
3.5%
krunal 4
 
2.8%
arshdeep 4
 
2.8%
singh 4
 
2.8%
josh 3
 
2.1%
hazlewood 3
 
2.1%
varun 3
 
2.1%
chakravarthy 3
 
2.1%
Other values (68) 103
72.5%
2025-08-13T16:56:24.428230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 132
 
13.7%
r 81
 
8.4%
h 77
 
8.0%
71
 
7.4%
n 52
 
5.4%
e 50
 
5.2%
i 48
 
5.0%
s 43
 
4.5%
d 38
 
3.9%
u 35
 
3.6%
Other values (36) 336
34.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 963
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 132
 
13.7%
r 81
 
8.4%
h 77
 
8.0%
71
 
7.4%
n 52
 
5.4%
e 50
 
5.2%
i 48
 
5.0%
s 43
 
4.5%
d 38
 
3.9%
u 35
 
3.6%
Other values (36) 336
34.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 963
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 132
 
13.7%
r 81
 
8.4%
h 77
 
8.0%
71
 
7.4%
n 52
 
5.4%
e 50
 
5.2%
i 48
 
5.0%
s 43
 
4.5%
d 38
 
3.9%
u 35
 
3.6%
Other values (36) 336
34.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 963
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 132
 
13.7%
r 81
 
8.4%
h 77
 
8.0%
71
 
7.4%
n 52
 
5.4%
e 50
 
5.2%
i 48
 
5.0%
s 43
 
4.5%
d 38
 
3.9%
u 35
 
3.6%
Other values (36) 336
34.9%

best_bowling_figure
Text

Missing 

Distinct54
Distinct (%)76.1%
Missing3
Missing (%)4.1%
Memory size4.5 KiB
2025-08-13T16:56:24.658620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.9859155
Min length4

Characters and Unicode

Total characters354
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)56.3%

Sample

1st row3--29
2nd row3--44
3rd row4--18
4th row3--42
5th row3--30
ValueCountFrequency (%)
2--25 4
 
5.6%
3--29 3
 
4.2%
2--18 2
 
2.8%
3--22 2
 
2.8%
3--21 2
 
2.8%
2--17 2
 
2.8%
3--24 2
 
2.8%
2--28 2
 
2.8%
3--33 2
 
2.8%
4--33 2
 
2.8%
Other values (44) 48
67.6%
2025-08-13T16:56:25.039531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 142
40.1%
3 58
16.4%
2 55
 
15.5%
4 33
 
9.3%
1 21
 
5.9%
5 13
 
3.7%
8 8
 
2.3%
7 8
 
2.3%
9 6
 
1.7%
6 6
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 354
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 142
40.1%
3 58
16.4%
2 55
 
15.5%
4 33
 
9.3%
1 21
 
5.9%
5 13
 
3.7%
8 8
 
2.3%
7 8
 
2.3%
9 6
 
1.7%
6 6
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 354
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 142
40.1%
3 58
16.4%
2 55
 
15.5%
4 33
 
9.3%
1 21
 
5.9%
5 13
 
3.7%
8 8
 
2.3%
7 8
 
2.3%
9 6
 
1.7%
6 6
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 354
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 142
40.1%
3 58
16.4%
2 55
 
15.5%
4 33
 
9.3%
1 21
 
5.9%
5 13
 
3.7%
8 8
 
2.3%
7 8
 
2.3%
9 6
 
1.7%
6 6
 
1.7%

Interactions

2025-08-13T16:56:14.307474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:08.682573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:09.454946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:10.460100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:11.285367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:12.070440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:12.937185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:14.475783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:08.806699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:09.561660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:10.578403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:11.402008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:12.213591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:13.088795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:14.621453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:08.915063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:09.680942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:10.702503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:11.508468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:12.337123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:13.266444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:14.771645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:09.044576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:09.806345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:10.822751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:11.625115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:12.449689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:13.433321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:14.922798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:09.142069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:09.922079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:10.931781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:11.724037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:12.556096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:13.582875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:15.063639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:09.243076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:10.034725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:11.045610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:11.843213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:12.662890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:13.728831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:15.225760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:09.354844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:10.344127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:11.171010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:11.946933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:12.772940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T16:56:14.152271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-08-13T16:56:25.156560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
balls_leftfirst_ings_scorefirst_ings_wktshighscorematch_idmatch_resultmatch_winnersecond_ings_scoresecond_ings_wktsstageteam1team2toss_decisiontoss_winnervenuewb_wickets
balls_left1.000-0.5300.266-0.236-0.0610.9710.075-0.450-0.5090.1080.0590.0360.2030.0000.0000.000
first_ings_score-0.5301.000-0.5960.5420.2020.3850.2050.6610.4530.0000.1810.1170.0000.0000.1820.000
first_ings_wkts0.266-0.5961.000-0.434-0.2760.1680.184-0.317-0.2130.0000.1790.1220.2260.1400.1970.202
highscore-0.2360.542-0.4341.0000.0351.0000.0000.579-0.0600.0690.0000.0000.2300.0000.0000.000
match_id-0.0610.202-0.2760.0351.0000.2890.0000.1420.1320.3400.0000.0000.1840.0000.0000.178
match_result0.9710.3850.1681.0000.2891.0001.0000.9560.3380.0000.0000.0000.0000.0000.0001.000
match_winner0.0750.2050.1840.0000.0001.0001.0000.1220.0000.0000.2840.4760.1300.4960.2120.000
second_ings_score-0.4500.661-0.3170.5790.1420.9560.1221.000-0.0050.0000.0000.0000.1810.0000.0000.000
second_ings_wkts-0.5090.453-0.213-0.0600.1320.3380.000-0.0051.0000.0480.0990.1490.0000.1600.1040.965
stage0.1080.0000.0000.0690.3400.0000.0000.0000.0481.0000.1040.1040.0000.0000.0130.000
team10.0590.1810.1790.0000.0000.0000.2840.0000.0990.1041.0000.0000.1850.2240.8640.000
team20.0360.1170.1220.0000.0000.0000.4760.0000.1490.1040.0001.0000.0340.5650.0000.216
toss_decision0.2030.0000.2260.2300.1840.0000.1300.1810.0000.0000.1850.0341.0000.1490.1940.317
toss_winner0.0000.0000.1400.0000.0000.0000.4960.0000.1600.0000.2240.5650.1491.0000.2240.000
venue0.0000.1820.1970.0000.0000.0000.2120.0000.1040.0130.8640.0000.1940.2241.0000.072
wb_wickets0.0000.0000.2020.0000.1781.0000.0000.0000.9650.0000.0000.2160.3170.0000.0721.000

Missing values

2025-08-13T16:56:15.511754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-13T16:56:15.801414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-08-13T16:56:16.236403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

match_iddatevenueteam1team2stagetoss_winnertoss_decisionfirst_ings_scorefirst_ings_wktssecond_ings_scoresecond_ings_wktsmatch_resultmatch_winnerwb_runswb_wicketsballs_leftplayer_of_the_matchtop_scorerhighscorebest_bowlingbest_bowling_figure
01March 22,2025Eden Gardens, KolkataKKRRCBLeagueRCBBowl174.08.0177.03.0completedRCBNaN722.0Krunal PandyaVirat Kohli59.0Krunal Pandya3--29
12March 23,2025Rajiv Gandhi International Stadium, HyderabadSRHRRLeagueRRBowl286.06.0242.06.0completedSRH44NaN0.0Ishan KishanIshan Kishan106.0Tushar Deshpande3--44
23March 23,2025MA Chidambaram Stadium, ChennaiCSKMILeagueCSKBowl155.09.0158.06.0completedCSKNaN45.0Noor AhmadRachin Ravindra65.0Noor Ahmad4--18
34March 24,2025ACA-VDCA Cricket Stadium, VishakhapatnamDCLSGLeagueDCBowl209.08.0211.09.0completedDCNaN13.0Ashutosh SharmaNicholas Pooran75.0Mitchell Starc3--42
45March 25,2025Narendra Modi Stadium, AhmedabadGTPBKSLeagueGTBowl243.05.0232.05.0completedPBKS11NaN0.0Shreyas IyerShreyas Iyer97.0Sai Kishore3--30
56March 26,2025Barsapara Stadium, GuwahatiRRKKRLeagueKKRBowl151.09.0153.02.0completedKKRNaN815.0Quinton de KockQuinton de Kock97.0Varun Chakravarthy2--17
67March 27,2025Rajiv Gandhi International Stadium, HyderabadSRHLSGLeagueLSGBowl190.09.0193.05.0completedLSGNaN523.0Shardul ThakurNicholas Pooran70.0Shardul Thakur4--34
78March 28,2025MA Chidambaram Stadium, ChennaiCSKRCBLeagueCSKBowl196.07.0146.08.0completedRCB50NaN0.0Rajat PatidarRajat Patidar51.0Josh Hazlewood3--21
89March 29,2025Narendra Modi Stadium, AhmedabadGTMILeagueMIBowl196.08.0160.06.0completedGT36NaN0.0Prasidh KrishnaSai Sudarshan63.0Prasidh Krishna2--18
910March 30,2025ACA-VDCA Cricket Stadium, VishakhapatnamDCSRHLeagueSRHBat163.010.0166.03.0completedDCNaN724.0Mitchell StarcAniket Verma74.0Mitchell Starc5--35
match_iddatevenueteam1team2stagetoss_winnertoss_decisionfirst_ings_scorefirst_ings_wktssecond_ings_scoresecond_ings_wktsmatch_resultmatch_winnerwb_runswb_wicketsballs_leftplayer_of_the_matchtop_scorerhighscorebest_bowlingbest_bowling_figure
6465May 23,2025Ekana Cricket Stadium, LucknowRCBSRHLeagueRCBBowl231.06.0189.010.0completedSRH42NaN1.0Ishan KishanIshan Kishan94.0Pat Cummins3--28
6566May 24,2025Sawai Mansingh Stadium, JaipurPBKSDCLeagueDCBowl206.08.0208.04.0completedDCNaN63.0Sameer RizviSameer Rizvi58.0Mustafizur Rahman3--33
6667May 25,2025Narendra Modi Stadium, AhmedabadGTCSKLeagueCSKBat230.05.0147.010.0completedCSK83NaN9.0Dewald BrevisDewald Brevis57.0Noor Ahmad3--21
6768May 25,2025Arun Jaitley Stadium, DelhiKKRSRHLeagueSRHBat278.03.0168.010.0completedSRH110NaN8.0Heinrich KlassenHeinrich Klassen105.0Jaydev Unadkat3--24
6869May 26,2025Sawai Mansingh Stadium, JaipurPBKSMILeaguePBKSBowl184.07.0187.03.0completedPBKSNaN79.0Josh InglisJosh Inglis73.0Arshdeep Singh2--28
6970May 27,2025Ekana Cricket Stadium, LucknowLSGRCBLeagueRCBBowl227.03.0230.04.0completedRCBNaN68.0Jitesh SharmaRishabh Pant118.0Will O'Rourke2--74
7071May 29,2025New PCA Cricket Stadium, MullanpurPBKSRCBPlayoffsRCBBowl101.010.0106.02.0completedRCBNaN860.0Suyash SharmaPhil Salt56.0Suyash Sharma3--17
7172May 30,2025New PCA Cricket Stadium, MullanpurGTMIPlayoffsMIBat228.05.0208.06.0completedMI20NaN0.0Rohit SharmaRohit Sharma81.0Sai Kishore2--42
7273June 1,2025Narendra Modi Stadium, AhmedabadPBKSMIPlayoffsPBKSBowl203.06.0207.05.0completedPBKSNaN56.0Shreyas IyerShreyas Iyer87.0Azmatullah Omarzai2--43
7374June 3,2025Narendra Modi Stadium, AhmedabadRCBPBKSFinalPBKSBowl190.09.0184.07.0completedRCB6NaN0.0Krunal PandyaShashank Singh61.0Arshdeep Singh3--40